Imperial College London
Alternate

STEFANOS ZAFEIRIOU, PhD

Faculty of Engineering, Department of Computing

Professor in Machine Learning & Computer Vision
 
 
 
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Contact

 

+44 (0)20 7594 8461s.zafeiriou Website CV

 
 
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Location

 

375Huxley BuildingSouth Kensington Campus

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Summary

 

Publications

Citation

BibTex format

@inproceedings{Chrysos:2017:10.1109/CVPRW.2017.252,
author = {Chrysos, GG and Zafeiriou, S},
doi = {10.1109/CVPRW.2017.252},
pages = {2015--2024},
publisher = {IEEE},
title = {Deep face deblurring},
url = {http://dx.doi.org/10.1109/CVPRW.2017.252},
year = {2017}
}
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RIS format (EndNote, RefMan)

TY  - CPAPER
AB  - Blind deblurring consists a long studied task, however the outcomes of generic methods are not effective in real world blurred images. Domain-specific methods for deblurring targeted object categories, e.g. text or faces, frequently outperform their generic counterparts, hence they are attracting an increasing amount of attention. In this work, we develop such a domain-specific method to tackle deblurring of human faces, henceforth referred to as face deblurring. Studying faces is of tremendous significance in computer vision, however face deblurring has yet to demonstrate some convincing results. This can be partly attributed to the combination of i) poor texture and ii) highly structure shape that yield the contour/gradient priors (that are typically used) sub-optimal. In our work instead of making assumptions over the prior, we adopt a learning approach by inserting weak supervision that exploits the well-documented structure of the face. Namely, we utilise a deep network to perform the deblurring and employ a face alignment technique to pre-process each face. We additionally surpass the requirement of the deep network for thousands training samples, by introducing an efficient framework that allows the generation of a large dataset. We utilised this framework to create 2MF2, a dataset of over two million frames. We conducted experiments with real world blurred facial images and report that our method returns a result close to the sharp natural latent image.
AU  - Chrysos,GG
AU  - Zafeiriou,S
DO  - 10.1109/CVPRW.2017.252
EP  - 2024
PB  - IEEE
PY  - 2017///
SN  - 2160-7508
SP  - 2015
TI  - Deep face deblurring
UR  - http://dx.doi.org/10.1109/CVPRW.2017.252
UR  - http://hdl.handle.net/10044/1/58200
ER  -
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